Learning Transformation Rules to Find Grammatical Relations

Grammatical relationships are an important level of natural language processing. We present a trainable approach to find these relationships through transformation sequences and error-driven learning. Our approach finds grammatical relationships between core syntax groups and bypasses much of the parsing phase. On our training and test set, our procedure achieves 63.6% recall and 77.3% precision (f-score = 69.8).Comment: 10 pages. Uses latex-acl.sty and named.st

P-model Alternative to the T-model

Standard linguistic analysis of syntax uses the T-model. This model requires the ordering: D-structure $>$ S-structure $>$ LF. Between each of these representations there is movement which alters the order of the constituent words; movement is achieved using the principles and parameters of syntactic theory. Psychological serial models do not accommodate the T-model immediately so that here a new model called the P-model is introduced. Here it is argued that the LF representation should be replaced by a variant of Frege's three qualities. In the F-representation the order of elements is not necessarily the same as that in LF and it is suggested that the correct ordering is: F-representation $>$ D-structure $>$ S-structure. Within this framework movement originates as the outcome of emphasis applied to the sentence.Comment: 28 pages, 73262 bytes, six eps diagrams, 53 references, background to this work is described: http://cosmology.mth.uct.ac.za/~roberts/pastresearch/pmodel.htm